voc1

Parallel WaveGAN with CSMSC

This example contains code used to train a parallel wavegan model with Chinese Standard Mandarin Speech Copus.

Dataset

Download and Extract

Download CSMSC from it's official website and extract it to ~/datasets. Then the dataset is in the directory ~/datasets/BZNSYP.

After processing the data, the BZNSYP directory will look like this:

BZNSYP
├── Wave
│    └─ *.wav files (audio speech)
├── PhoneLabeling
│    └─ *.interval files (alignment between phoneme and duration)
└── ProsodyLabeling
     └─ 000001-010000.txt (text with prosodic by pinyin)

This experiment only uses *.wav files from the Wave file

Get MFA Result and Extract

We use MFA results to cut silence at the edge of audio. You can download from here baker_alignment_tone.tar.gz, or train your MFA model reference to mfa example of our repo.

Get Started

Assume the path to the dataset is ~/datasets/BZNSYP. Assume the path to the MFA result of CSMSC is ./baker_alignment_tone. Run the command below to

1. source path.
2. preprocess the dataset.
3. train the model.
4. synthesize wavs.
   • synthesize waveform from metadata.jsonl.
   • synthesize waveform from text file.

./run.sh

You can choose a range of stages you want to run, or set stage equal to stop-stage to use only one stage, for example, running the following command will only preprocess the dataset.

./run.sh --stage 0 --stop-stage 0

Data Preprocessing

./local/preprocess.sh ${conf_path}

When it is done. A dump folder is created in the current directory. The structure of the dump folder is listed below.

dump
├── dev
│   ├── norm
│   └── raw
├── test
│   ├── norm
│   └── raw
└── train
    ├── norm
    ├── raw
    └── feats_stats.npy

The dataset is split into 3 parts, namely train, dev, and test, each of which contains a norm and raw subfolder. The raw folder contains the log magnitude of the mel spectrogram of each utterance, while the norm folder contains the normalized spectrogram. The statistics used to normalize the spectrogram are computed from the training set, which is located in dump/train/feats_stats.npy.

Also, there is a metadata.jsonl in each subfolder. It is a table-like file that contains id and paths to the spectrogram of each utterance.

Model Training

CUDA_VISIBLE_DEVICES=${gpus} ./local/train.sh ${conf_path} ${train_output_path}

./local/train.sh calls ${BIN_DIR}/train.py. Here's the complete help message.

usage: train.py [-h] [--config CONFIG] [--train-metadata TRAIN_METADATA]
                [--dev-metadata DEV_METADATA] [--output-dir OUTPUT_DIR]
                [--ngpu NGPU] [--batch-size BATCH_SIZE] [--max-iter MAX_ITER]
                [--run-benchmark RUN_BENCHMARK]
                [--profiler_options PROFILER_OPTIONS]

Train a ParallelWaveGAN model.

optional arguments:
  -h, --help            show this help message and exit
  --config CONFIG       ParallelWaveGAN config file.
  --train-metadata TRAIN_METADATA
                        training data.
  --dev-metadata DEV_METADATA
                        dev data.
  --output-dir OUTPUT_DIR
                        output dir.
  --ngpu NGPU           if ngpu == 0, use cpu.

benchmark:
  arguments related to benchmark.

  --batch-size BATCH_SIZE
                        batch size.
  --max-iter MAX_ITER   train max steps.
  --run-benchmark RUN_BENCHMARK
                        runing benchmark or not, if True, use the --batch-size
                        and --max-iter.
  --profiler_options PROFILER_OPTIONS
                        The option of profiler, which should be in format
                        "key1=value1;key2=value2;key3=value3".

1. --config is a config file in yaml format to overwrite the default config, which can be found at conf/default.yaml.
2. --train-metadata and --dev-metadata should be the metadata file in the normalized subfolder of train and dev in the dump folder.
3. --output-dir is the directory to save the results of the experiment. Checkpoints are saved in checkpoints/ inside this directory.
4. --ngpu is the number of gpus to use, if ngpu == 0, use cpu.

Synthesizing

We use parallel wavegan as the neural vocoder. Download pretrained parallel wavegan model from pwg_baker_ckpt_0.4.zip and unzip it.

unzip pwg_baker_ckpt_0.4.zip

Parallel WaveGAN checkpoint contains files listed below.

pwg_baker_ckpt_0.4
├── pwg_default.yaml               # default config used to train parallel wavegan
├── pwg_snapshot_iter_400000.pdz   # model parameters of parallel wavegan
└── pwg_stats.npy                  # statistics used to normalize spectrogram when training parallel wavegan

./local/synthesize.sh calls ${BIN_DIR}/../synthesize.py, which can synthesize waveform from metadata.jsonl.

CUDA_VISIBLE_DEVICES=${gpus} ./local/synthesize.sh ${conf_path} ${train_output_path} ${ckpt_name}

usage: synthesize.py [-h] [--generator-type GENERATOR_TYPE] [--config CONFIG]
                     [--checkpoint CHECKPOINT] [--test-metadata TEST_METADATA]
                     [--output-dir OUTPUT_DIR] [--ngpu NGPU]

Synthesize with GANVocoder.

optional arguments:
  -h, --help            show this help message and exit
  --generator-type GENERATOR_TYPE
                        type of GANVocoder, should in {pwgan, mb_melgan,
                        style_melgan, } now
  --config CONFIG       GANVocoder config file.
  --checkpoint CHECKPOINT
                        snapshot to load.
  --test-metadata TEST_METADATA
                        dev data.
  --output-dir OUTPUT_DIR
                        output dir.
  --ngpu NGPU           if ngpu == 0, use cpu.

1. --config parallel wavegan config file. You should use the same config with which the model is trained.
2. --checkpoint is the checkpoint to load. Pick one of the checkpoints from checkpoints inside the training output directory.
3. --test-metadata is the metadata of the test dataset. Use the metadata.jsonl in the dev/norm subfolder from the processed directory.
4. --output-dir is the directory to save the synthesized audio files.
5. --ngpu is the number of gpus to use, if ngpu == 0, use cpu.

We use Fastspeech2 as the acoustic model. Download pretrained fastspeech2_nosil model from fastspeech2_nosil_baker_ckpt_0.4.zipand unzip it.

unzip fastspeech2_nosil_baker_ckpt_0.4.zip

Fastspeech2 checkpoint contains files listed below.

fastspeech2_nosil_baker_ckpt_0.4
├── default.yaml            # default config used to train fastspeech2
├── phone_id_map.txt        # phone vocabulary file when training fastspeech2
├── snapshot_iter_76000.pdz # model parameters and optimizer states
└── speech_stats.npy        # statistics used to normalize spectrogram when training fastspeech2

./local/synthesize_e2e.sh calls ${BIN_DIR}/../synthesize_e2e.py, which can synthesize waveform from text file.

CUDA_VISIBLE_DEVICES=${gpus} ./local/synthesize_e2e.sh ${conf_path} ${train_output_path} ${ckpt_name}

usage: synthesize_e2e.py [-h]
                         [--am {speedyspeech_csmsc,speedyspeech_aishell3,fastspeech2_csmsc,fastspeech2_ljspeech,fastspeech2_aishell3,fastspeech2_vctk,tacotron2_csmsc,tacotron2_ljspeech}]
                         [--am_config AM_CONFIG] [--am_ckpt AM_CKPT]
                         [--am_stat AM_STAT] [--phones_dict PHONES_DICT]
                         [--tones_dict TONES_DICT]
                         [--speaker_dict SPEAKER_DICT] [--spk_id SPK_ID]
                         [--voc {pwgan_csmsc,pwgan_ljspeech,pwgan_aishell3,pwgan_vctk,mb_melgan_csmsc,style_melgan_csmsc,hifigan_csmsc,hifigan_ljspeech,hifigan_aishell3,hifigan_vctk,wavernn_csmsc}]
                         [--voc_config VOC_CONFIG] [--voc_ckpt VOC_CKPT]
                         [--voc_stat VOC_STAT] [--lang LANG]
                         [--inference_dir INFERENCE_DIR] [--ngpu NGPU]
                         [--text TEXT] [--output_dir OUTPUT_DIR]

Synthesize with acoustic model & vocoder

optional arguments:
  -h, --help            show this help message and exit
  --am {speedyspeech_csmsc,speedyspeech_aishell3,fastspeech2_csmsc,fastspeech2_ljspeech,fastspeech2_aishell3,fastspeech2_vctk,tacotron2_csmsc,tacotron2_ljspeech}
                        Choose acoustic model type of tts task.
  --am_config AM_CONFIG
                        Config of acoustic model.
  --am_ckpt AM_CKPT     Checkpoint file of acoustic model.
  --am_stat AM_STAT     mean and standard deviation used to normalize
                        spectrogram when training acoustic model.
  --phones_dict PHONES_DICT
                        phone vocabulary file.
  --tones_dict TONES_DICT
                        tone vocabulary file.
  --speaker_dict SPEAKER_DICT
                        speaker id map file.
  --spk_id SPK_ID       spk id for multi speaker acoustic model
  --voc {pwgan_csmsc,pwgan_ljspeech,pwgan_aishell3,pwgan_vctk,mb_melgan_csmsc,style_melgan_csmsc,hifigan_csmsc,hifigan_ljspeech,hifigan_aishell3,hifigan_vctk,wavernn_csmsc}
                        Choose vocoder type of tts task.
  --voc_config VOC_CONFIG
                        Config of voc.
  --voc_ckpt VOC_CKPT   Checkpoint file of voc.
  --voc_stat VOC_STAT   mean and standard deviation used to normalize
                        spectrogram when training voc.
  --lang LANG           Choose model language. zh or en
  --inference_dir INFERENCE_DIR
                        dir to save inference models
  --ngpu NGPU           if ngpu == 0, use cpu.
  --text TEXT           text to synthesize, a 'utt_id sentence' pair per line.
  --output_dir OUTPUT_DIR
                        output dir.

1. --am is acoustic model type with the format {model_name}_{dataset}
2. --am_config, --am_ckpt, --am_stat and --phones_dict are arguments for acoustic model, which correspond to the 4 files in the fastspeech2 pretrained model.
3. --voc is vocoder type with the format {model_name}_{dataset}
4. --voc_config, --voc_ckpt, --voc_stat are arguments for vocoder, which correspond to the 3 files in the parallel wavegan pretrained model.
5. --lang is the model language, which can be zh or en.
6. --test_metadata should be the metadata file in the normalized subfolder of test in the dump folder.
7. --text is the text file, which contains sentences to synthesize.
8. --output_dir is the directory to save synthesized audio files.
9. --ngpu is the number of gpus to use, if ngpu == 0, use cpu.

Pretrained Models

The pretrained model can be downloaded here:

• pwg_baker_ckpt_0.4.zip
• fastspeech2_nosil_baker_ckpt_0.4.zip

The static model can be downloaded here:

• pwg_baker_static_0.4.zip
• fastspeech2_nosil_baker_static_0.4.zip

The ONNX model can be downloaded here:

• pwgan_csmsc_onnx_0.2.0.zip
• fastspeech2_csmsc_onnx_0.2.0.zip

The Paddle-Lite model can be downloaded here:

• pwgan_csmsc_pdlite_1.3.0.zip
• fastspeech2_csmsc_pdlite_1.3.0.zip

Model	Step	eval/generator_loss	eval/log_stft_magnitude_loss	eval/spectral_convergence_loss
default	1(gpu) x 400000	1.948763	0.670098	0.248882

Parallel WaveGAN checkpoint contains files listed below.

pwg_baker_ckpt_0.4
├── pwg_default.yaml              # default config used to train parallel wavegan
├── pwg_snapshot_iter_400000.pdz  # generator parameters of parallel wavegan
└── pwg_stats.npy                 # statistics used to normalize spectrogram when training parallel wavegan

FastSpeech2 checkpoint contains files listed below.

fastspeech2_nosil_baker_ckpt_0.4
├── default.yaml            # default config used to train fastspeech2
├── phone_id_map.txt        # phone vocabulary file when training fastspeech2
├── snapshot_iter_76000.pdz # model parameters and optimizer states
└── speech_stats.npy        # statistics used to normalize spectrogram when training fastspeech2

Acknowledgement

We adapted some code from https://github.com/kan-bayashi/ParallelWaveGAN.